seaborn:
it is a python library which is used to build interactive vizulaizations
this library makes us easy to understand the statistical representation
we can easily understand the patterns and relationships inside the data
with the help of this vizualizations we can easily take some data driven decisions

seaborn has different categories while plotting the data
categorical plots
distribution plots
regression plots
matrix plots
relational plots
multi plot grids

categorical plots:
it is used to vizualize the data on different categories

barplot()
boxplot()
violinplot()
countplot()
stripplot()
factorplot()

distribution plots:
whenever we want to see the distributions between univaraite and bivariate analysis

jointplot
distplot
pairplot
rugplot

regression plot:
it is used to show the data columns relationships in a linear way

lmplot
regplot

matrix plot:

it is used to show the data in a matrix form

heatmap

relational plot:
it is used to show the relationships between two variables

relplot()
scatterplot()
lineplot()

multi plot grids:

it is used to vizualize multiple instances of the same plot on different subsets of data

facetgrid

barplot:it is used to display the aggregated values(mean,min,max) for different categories of data
lineplot:it is used to display the relationship between two numeric columns in a continuous line
scatterplot:it is used to show the relationship between two columns(input/output)
countplot:it is used to show the count of observations in each category
histogram:it is used to show the distributions of the data
kde:it is used to show the kernel density estimator how the curve of the data is representating the viz
boxplot:used to display the outliers inside the data
violinplot:combination of boxplot and kde plot
heatmap:it is used to show the correlation of the data in a matrix form
pairplot:used to  show all the numeric columns(scatterplot,kde,hist)
lmpoint:scatter plot with regression line

pip install seaborn

import seaborn as sns

import numpy as np
import pandas as pd

# Set random seed for reproducibility
np.random.seed(42)

# Create synthetic data
n = 300

data = pd.DataFrame({
    "Date": pd.date_range(start="2024-01-01", periods=n, freq="D"),
    "Region": np.random.choice(["North", "South", "East", "West"], n),
    "Category": np.random.choice(["Electronics", "Clothing", "Home", "Sports"], n),
    "Sales": np.random.normal(500, 120, n).round(2),
    "Profit": np.random.normal(80, 30, n).round(2),
    "Quantity": np.random.randint(1, 20, n),
    "Discount": np.random.uniform(0, 0.3, n).round(2),
    "Customer_Age": np.random.randint(18, 65, n)
})

data

sns.histplot(data["Sales"],kde=True)

<Axes: xlabel='Sales', ylabel='Count'>

sns.boxplot(x="Category",y="Sales",data=data)

<Axes: xlabel='Category', ylabel='Sales'>

sns.barplot(x="Region",y="Profit",data=data)

<Axes: xlabel='Region', ylabel='Profit'>

sns.scatterplot(x="Discount",y="Sales",data=data)

<Axes: xlabel='Discount', ylabel='Sales'>

sns.countplot(x="Region",data=data)

<Axes: xlabel='Region', ylabel='count'>

sns.jointplot(data)

<seaborn.axisgrid.JointGrid at 0x1a47ad158b0>

sns.pairplot(data=data)

<seaborn.axisgrid.PairGrid at 0x1a47ab6cec0>

what is seaborn how it is different from matplotlib?
how to install seaborn library?
what is the difference between countplot() and barplot()
how do you create a boxplot in seaborn?
what is the use of hue parameter?
what is kdeplot?
what is the use of jointplot?
what is pairplot() what is the importance of pairplot?
what is the difference between distplot() and histplot()?
how do you create heatmap?
what are the different categories available in seaborn library?
how do we create a histplot?
how do you vizulaize the correlation matrix?

scenerio based questions:
1.sales analysis
you need to create a sales data where the data should contains order date

you have monthly sales for 5 years

which plot will use to show the trends?

how will we compare sales across different regions?


2.Hr dataset

we want to check salary distribution by department

which plot you will use and why?
how to check the outliers in the dataset?
what is the importance of outliers and how it will impact on ml models?


3.health care dataset

cancer dataset

how many people affected with cancer?
gender(male/female)---cancer affecting gender
age(child/adult)---cancer affecting age group


4.banking dataset

you want to check correlation between the features

how will you vizualize correlation?
which plot should be use and why?
what is correlation?
how you can understand whether your data is strongly correlated or not

dashboard---multiple plots

streamlit ---web application

dashboard with the help of library i.e plotly

plotly:it is one of the library which is used to vizualize the data in more interactive way

pip install plotly

import plotly.express as px

d1=px.bar(data,x="Category",y="Sales",color="Region")

	Date	Region	Category	Sales	Profit	Quantity	Discount	Customer_Age
0	2024-01-01	East	Electronics	505.47	78.02	4	0.05	25
1	2024-01-02	West	Electronics	421.81	43.67	18	0.26	39
2	2024-01-03	North	Home	757.27	60.44	5	0.07	61
3	2024-01-04	East	Clothing	576.07	81.42	16	0.29	53
4	2024-01-05	East	Clothing	256.98	54.19	1	0.10	22
...	...	...	...	...	...	...	...	...
295	2024-10-22	West	Sports	505.77	64.31	14	0.30	42
296	2024-10-23	South	Electronics	531.17	67.39	11	0.20	26
297	2024-10-24	East	Home	391.48	71.55	18	0.17	19
298	2024-10-25	North	Sports	576.63	39.67	12	0.22	32
299	2024-10-26	West	Clothing	300.62	52.44	12	0.14	20